Wearable tech, AI and clinical teams combine to change the face of clinical trial monitoring

In a ground-breaking study, published in Nature Medicine, a cross-disciplinary team of AI and clinical researchers have shown that by combining human movement data gathered from wearable tech with a powerful new medical AI technology they are able to identify clear movement patterns, predict future disease progression and significantly increase the efficiency of clinical trials in a rare disorder: Friedreich's ataxia (FA).

Tracking the progression of FA is normally done through intensive testing in a clinical setting. This paper offers a significantly more precise assessment that also increases the accuracy and objectivity of the data collected.

FA is a rare, degenerative, genetic disease that affect movement and eventually lead to paralysis. There is currently no cure for the disease, but researchers hope that these results will significantly speed up the search for new treatments.

The researchers estimate that using these disease markers means that significantly fewer patients are required to develop a new drug when compared to current methods. This is particularly important for rare diseases where it can be hard to identify suitable patients.

Scientists hope that as well as using the technology to monitor patients in clinical trials, it could also one day be used to monitor or diagnose a range of common diseases that affect movement behaviour such as dementia, stroke and orthopaedic conditions.

Movement fingerprints – the trial in detail

In the FA study, teams at the UCL Ataxia Centre, UCL Queen Square Institute of Neurology, and Imperial College London, worked with patients to identify key movement patterns and predict genetic markers of disease. They were able to administer a rating scale to determine level of disability of ataxia SARA and functional assessments like walking, hand/arms movements (SCAFI) in nine FA patients and matching controls.

The results of these validated clinical assessments were then compared with the one obtained from using the novel technology on the same patients and controls. The latter showing more sensitivity in predicting disease progression.

FA is the most common inherited ataxia and is caused by the decrease of a protein called frataxin due to a genetic mutation. The mutation, a GAA expansion, “switches off” the gene that variably reduces the level of frataxin in patients. Using this new AI technology, the team were able to use movement data to accurately predict the level of frataxin that differs in each patient, without the need to take biological samples. The two standard clinical assessments, SARA and SCAFI, failed to predict the frataxin level in patients.

All the data from the sensors was collected and fed into the AI technology to create individual avatars and analyse movements. This vast data set and powerful computing tool allowed researchers to define key movement fingerprints seen in adults with FA, that were different in the control group. Many of these AI-based movement patterns had not been described clinically before in FA.

Scientists also discovered that the new AI technique could also significantly improve predictions of how individual patients’ disease would progress compared to current gold-standard assessments. Such a precise prediction has the potential to allow clinical trials to run more efficiently so that patients can access novel therapies quicker, and also help dose drugs more precisely.

Image: FA patient Yanita Oparlakova wearing AI technology

Co-author Professor Paola Giunti, Head of UCL Ataxia Centre, Queen Square Institute of Neurology, and Honorary Consultant at the National Hospital for Neurology and Neurosurgery, UCLH, said: “We are thrilled with the results of this project that showed how AI approaches are certainly superior in capturing progression of the disease in a rare disease like Friedreich’s ataxia. With this novel approach we can potentially revolutionise clinical trial design for new drugs and monitor the effects of already existing drugs with an accuracy that was unknown with previous methods.

“The large number of FA patients who were very well characterised both clinically and genetically at the Ataxia Centre UCL Queen Square Institute of Neurology in addition to our crucial input on the clinical protocol has made the project possible. We are also grateful to all our patients who participated in this project.”

Co-author, Dr Suran Nethisinghe, the UCL Ataxia Centre, UCL Queen Square Institute of Neurology, said: “I’m delighted to have been involved in this exciting study, for which I characterised the genetic mutation (a GAA repeat expansion) and disease modifier that are responsible for the Friedreich’s ataxia patients' phenotype. Use of this novel genetic modifier more precisely predicted the level of frataxin in these patients. This is a very rare condition, but the Ataxia Centre participated in a natural history study clinically and genetically characterising more than 300 patients, which enabled us to select a very homogenous group of ambulatory patients to validate the sensor and AI tool. This was essential to the success of the study, which will have huge implications for future clinical trials.”

Co-author, Dr Valeria Ricotti, an honorary clinical lecturer at UCL GOS ICH said: “Researching rare conditions can be substantially more costly and logistically challenging, which means that patients are missing out on potential new treatments. Increasing the efficiency of clinical trials gives us hope that we can test many more treatments successfully.”

Co-author, Professor Thomas Voit, a Professor of Developmental Neurosciences at UCL GOS ICH, and Director of the NIHR Great Ormond Street Biomedical Research Centre (NIHR GOSH BRC) said: “These studies show how innovative technology can significantly improve the way we study diseases day-to-day. The impact of this, alongside specialised clinical knowledge, will not only improve the efficiency of clinical trials but has the potential to translate across a huge variety of conditions that impact movement. It is thanks to collaborations across research institutes, hospitals, clinical specialities and with dedicated patients and families that we can start solving the challenging problems facing rare disease research.”  

Smaller numbers for future clinical trials

This new way of analysing full-body movement measurements provide clinical teams with clear disease markers and progression predictions. These are invaluable tools during clinical trials to measure the benefits of new treatments.

The new technology could help researchers carry out clinical trials of conditions that affect movement more quickly and accurately. In the FA study, the researchers showed that they could achieve the same precision with  fewwer patients. This AI technology is especially powerful when studying rare diseases, when patient populations are smaller. Importantly, the technology facilitates the study of patients who are more disabled allowing more severely affected  children to be recruited in clinical trials.

Senior and corresponding author, Professor Aldo Faisal, from Imperial College London’s Departments of Bioengineering and Computing, who is also Director of the UKRI Centre for Doctoral Training in AI for Healthcare, said: “Our approach gathers huge amounts of data from a person’s full-body movement – more than any neurologist will have the precision or time to observe a patient. Our AI technology builds a digital twin of the patient and allows us to make unprecedented, precise predictions of how an individual patient’s disease will progress. We believe that the same AI technology working in two very different diseases, shows how promising it is to be applied to many diseases and help us to develop treatments for many more diseases even faster, cheaper and more precisely.”

Co-author, Professor Richard Festenstein, from the MRC London Institute of Medical Sciences and Department of Brain Sciences at Imperial College London said: “Patients and families often want to know how their disease is progressing, and motion capture technology combined with AI could help to provide this information. We’re hoping that this research has the potential to transform clinical trials in rare movement disorders, as well as improve diagnosis and monitoring for patients above human performance levels.”

This paper highlights the work of a collaboration of researchers and expertise, across AI technology, engineering, genetics and clinical specialties. These include researchers at the Ataxia Centre UCL/UCLH at UCL Queen Square Institute of Neurology, UCL Great Ormond Street Institute for Child Health, the NIHR Great Ormond Street Hospital Biomedical Research Centre (NIHR GOSH BRC), the National Hospital for Neurology and Neurosurgery (UCLH), Great Ormond Street Hospital, the Department of Bioengineering, the Department of Computing and the UKRI Centre in AI for Healthcare at Imperial College London, MRC London Institute of Medical Sciences (MRC LMS), the University of Bayreuth in Germany and the Gemelli Hospital in Rome, Italy.

Links 

• Kadirvelu, B., Gavriel, C., Nageshwaran, S. et al. A wearable motion capture suit and machine learning predict disease progression in Friedreich’s ataxia. Nat Med (2023). https://doi.org/10.1038/s41591-022-02159-6
• Professor Paola Giunti’s academic profile  
• Dr Suran Nethisinghe's academic proflle
• UCL Ataxia Centre, UCL Queen Square Institute of Neurology